CASIA OpenIR
Learning view invariant gait features with Two-Stream GAN
Wang, Yanyun1,2; Song, Chunfeng2,3; Huang, Yan2,3,4; Wang, Zhenyu1; Wang, Liang2,3,4
Source PublicationNEUROCOMPUTING
ISSN0925-2312
2019-04-28
Volume339Issue:2019Pages:245-254
Abstract

Gait recognition is an important yet challenging problem in computer vision. The changing view of gait is one of the most challenging factors, which could greatly affect the accuracy of cross-view gait recognition. In this paper, we propose a Two-Stream Generative Adversarial Network (TS-GAN) for cross-view gait recognition. For any view of gait representations, GAN can restore it to the corresponding standard view, to learn view invariant gait features. To achieve this goal, TS-GAN has two streams : (1) the global-stream can learn global contexts, and (2) the part-stream can learn local details. We combine the two streams to learn final identities. Moreover, we add a pixel-wise loss along with the generators of GAN to restore the gait details in pixel-level. We evaluate the proposed method on two widely used gait databases: CASIA-B and OU-ISIR. Experiment results show that our approach outperforms the compared state-of-the-art approaches. (C) 2019 Elsevier B.V. All rights reserved.

KeywordGait recognition Cross-veiw Two-Stream GAN
DOI10.1016/j.neucom.2019.02.025
WOS KeywordRECOGNITION ; REPRESENTATION ; IMAGE
Indexed BySCI
Language英语
Funding ProjectFundamental Research Funds for the Central Universities[2018ZD05] ; National National Science Foundation of China[61420106015] ; National National Science Foundation of China[61721004] ; National National Science Foundation of China[61633021] ; National National Science Foundation of China[61525306] ; National National Science Foundation of China[61573139] ; National Key Research and Development Program of China[2016YFB10010 0 0] ; Beijing Science and Technology Project[Z181100008918010] ; Capital Science and Technology Leading Talent Training Project[Z181100006318030] ; Capital Science and Technology Leading Talent Training Project[Z181100006318030] ; Beijing Science and Technology Project[Z181100008918010] ; National Key Research and Development Program of China[2016YFB10010 0 0] ; National National Science Foundation of China[61573139] ; National National Science Foundation of China[61525306] ; National National Science Foundation of China[61633021] ; National National Science Foundation of China[61721004] ; National National Science Foundation of China[61420106015] ; Fundamental Research Funds for the Central Universities[2018ZD05]
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000461166500024
PublisherELSEVIER SCIENCE BV
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/25005
Collection中国科学院自动化研究所
Corresponding AuthorWang, Zhenyu
Affiliation1.North China Elect Power Univ, Sch Control & Comp Engn, Beijing 102206, Peoples R China
2.Chinese Acad Sci CASIA, CRIPAC, NLPR, Beijing 100190, Peoples R China
3.UCAS, Beijing 100190, Peoples R China
4.CEBSIT, Beijing 100190, Peoples R China
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Wang, Yanyun,Song, Chunfeng,Huang, Yan,et al. Learning view invariant gait features with Two-Stream GAN[J]. NEUROCOMPUTING,2019,339(2019):245-254.
APA Wang, Yanyun,Song, Chunfeng,Huang, Yan,Wang, Zhenyu,&Wang, Liang.(2019).Learning view invariant gait features with Two-Stream GAN.NEUROCOMPUTING,339(2019),245-254.
MLA Wang, Yanyun,et al."Learning view invariant gait features with Two-Stream GAN".NEUROCOMPUTING 339.2019(2019):245-254.
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